JECL can be considered as a form of multi-view clustering, except that multi-view methods often only consider only one data type, typically multiple images of the same object.
Matrix factorization is a common approach to address multi-view clustering problem. Liu et al. [1]} used a joint matrix factorization with restraints to progressively find the consensus between different views. Zhao et al. [2]} developed a deep matrix factorization framework which imposes the non-negative representation of all views to be the same in final layer to maximize the mutual information and is graph regularized to preserve the local geometric structure in each view. BMVC, binary multi-view clustering, is presented by Zhang et al [3]} to easily scale to large data by collective encoding views into a compact common binary code space and simultaneously clustering the collaborative binary representations using a matrix factorization model. Spectral clustering has shown significant performance in single view tasks and it also has been explored in multi-view scenario. Brbić et al. [4]} proposes a multi-view spectral clsutering framework that encourages sparsity and low-rank solution and balances the agreement across views. However, most spectral clustering methods are not scalable due to its quadratic complexity.
